Our 2026 AI Prediction

While others publish lists of ten or twenty predictions hoping to get at least one right, we're taking a different approach. We believe in the power of focus. This is our prediction (singular, not plural) because we've identified what we believe is the single most important trend that will shape the AI industry in 2026: the growing need for measuring the performance of AI.

If that sounds familiar, it should. We're witnessing a pattern that played out starting about twenty-five years ago in a different technological revolution.

The Web Analytics Revolution: A Mirror into the Past

In the early 2000s, the digital advertising industry faced a paradox. Companies were spending heavily on digital ads, yet very few were properly measuring performance or doing any sort of A/B testing. The tools existed: companies like WebTrends, Omniture, and Urchin Software Corporation were pioneering web analytics, but most organizations treated measurement as an afterthought rather than a strategic imperative.

Urchin, founded in the late 1990s, could process server logs in approximately 30 minutes while competitors took 24 hours or more. This technological advantage, combined with their focus on making complex analytics accessible to non-technical users, positioned them to serve major clients including Honda, NASA, and AT&T. Yet despite these early successes, the broader market remained immature. Most companies were flying blind, unable to connect their digital spending to actual business outcomes.

The tipping point came with consolidation. Google acquired Urchin in 2005 and transformed it into Google Analytics, eventually making it free and ubiquitous. What followed was a complete transformation of the industry. Web analytics evolved from a nice-to-have into table stakes. Today, it would be unthinkable for a company to run digital campaigns without tracking conversions, attribution, and incremental lift. The sophistication we now take for granted, such as A/B testing frameworks, multi-touch attribution models, and cohort analysis all emerged from this period of maturation and consolidation.

History Repeats: The AI Impact Gap

Fast forward to 2025, and the parallels are striking. Organizations are investing billions in AI initiatives, yet most struggle to connect these investments to tangible business outcomes. 76% of executives struggle to quantify the return on AI investment, a measurement gap that mirrors the early days of digital advertising.

The fundamental challenge is the same: AI doesn't just automate tasks; it changes how work happens, often in ways that are difficult to quantify using traditional metrics. MIT's research found that 95% of organizations studied are seeing zero return on their AI initiatives, not necessarily because the AI doesn't work, but because companies are measuring it with the wrong frameworks.

Just as early web analytics pioneers had to convince organizations that measuring clicks and conversions mattered, today's AI leaders are discovering that model accuracy or task completion rates tell only part of the story. The real value lies in understanding how AI features impact high-level business metrics: customer lifetime value, revenue per employee, operational efficiency, and ultimately, P&L.

The Consolidation Wave Begins

The market is already signaling maturity through consolidation, mirroring the pattern we saw with web analytics 2+ decades ago. Two significant acquisitions in 2025 underscore this trend:

Datadog's acquisition of Eppo in May 2025 for approximately $220 million further validates this trend. The acquisition creates an end-to-end product analytics solution integrating feature management, experimentation, and real-time observability. “The use of multiple AI models increases the complexity of deploying applications in production,” Datadog's VP of Product explained. Eppo's experimentation capabilities directly address this challenge.

OpenAI's acquisition of Statsig in September 2025 for $1.1 billion represents a strategic bet on experimentation infrastructure. Statsig powers A/B testing, feature flagging, and real-time decisioning for some of the world's most innovative companies. The acquisition signals that even OpenAI, the leader in foundation models, recognizes that building great AI products requires rigorous measurement and experimentation frameworks. While AI can create countless variations, Statsig provides the rigorous testing framework needed to determine what actually works in the real world.

These acquisitions aren't anomalies, they're harbingers. Just as Google's acquisition of Urchin catalyzed the web analytics industry's maturation, these moves by OpenAI and Datadog signal that AI measurement and experimentation are becoming foundational infrastructure rather than peripheral concerns.

The Growing Sophistication Gap

The companies getting AI right aren't just deploying models, they're building measurement systems. Organizations with mature AI ROI measurement practices achieve 4.2x higher returns compared to those without structured metrics frameworks, according to Gartner research.

Consider what mature AI measurement looks like in practice:

Iterative testing frameworks: Rather than deploying a single model and hoping for the best, leading organizations run continuous experiments comparing different models, prompts, and approaches against business outcomes. They're testing whether GPT-4 or Claude drives better customer satisfaction, whether a more expensive model delivers proportionally better results, or whether prompt variations affect conversion rates.

Attribution to business metrics: The most sophisticated teams have moved beyond vanity metrics like "AI adoption rate" or "number of AI features shipped." They're connecting AI performance directly to revenue, cost savings, customer retention, and other high-level KPIs. They can answer questions like: "Did implementing AI in customer support reduce churn?" or "What's the incremental revenue from AI-powered recommendations?"

Real-time observability: Just as modern web analytics provides real-time insights, AI measurement systems are evolving to offer immediate feedback on model performance, allowing teams to catch issues before they impact users and rapidly iterate toward better outcomes.

The gap between leaders and laggards will only widen. Recent data shows 42% of companies scrapped most of their AI initiatives in 2025, up sharply from just 17% the year before. The culprit isn't necessarily poor AI technology, it's the inability to measure what matters and iterate based on data.

Why 2026 Is the Inflection Point

Several converging factors make 2026 the year when AI measurement becomes critical:

Market maturity: AI is moving from experimentation to production at scale. 88 percent of survey respondents report regular AI use in at least one business function, compared with 78 percent a year ago, according to McKinsey. As deployments grow, so does the need for rigorous measurement.

Economic pressure: CFOs are demanding accountability for AI spending. Nearly three-quarters (72%) of business leaders now have a structured process in place for tracking returns on AI investments, and that percentage will only increase as budgets tighten.

Competitive dynamics: The companies that can rapidly test and iterate their AI features will pull ahead. Just as A/B testing became a competitive weapon in the web era, AI experimentation will separate winners from losers in 2026.

Platform convergence: Recent acquisitions signal that major platforms are building AI measurement directly into their offerings, making sophisticated analytics more accessible, just as Google Analytics democratized web analytics two decades ago. However, we’re still too early on this trend for the flood gates to open.

What This Means for Teams in 2026

For product teams, the message is clear: if you're building AI features without robust measurement frameworks, you're flying blind. The days of launching an AI feature and hoping for the best are over. In 2026, best practices will include:

• Pre-defining business metrics before building AI features, not after deployment

• Implementing experimentation frameworks that allow rapid testing of different approaches

• Building observability into AI systems from day one

• Connecting AI performance directly to P&L and other high-level business outcomes

• Creating feedback loops that enable continuous improvement based on real user data

For executives, the imperative is equally clear: investing in AI without investing in measurement infrastructure is throwing money away. The companies that will win aren't necessarily those with the best AI models, but those with the best systems for measuring, testing, and iterating on AI features.

The Pattern Holds

Twenty-five years ago, web analytics transformed from a niche technical concern into fundamental business infrastructure. Companies that embraced measurement early gained lasting competitive advantages. Those that lagged behind eventually caught up, but at great cost in lost opportunities and wasted spending.

We're at a similar inflection point with AI. The tools exist. The patterns are clear. The consolidation has begun. In 2026, the gap between organizations that measure their AI performance rigorously and those that don't will become impossible to ignore.

This is our prediction: the growing need for measuring AI performance, especially for iterative products and features, will be the defining trend of 2026. Not because it's flashy or generates headlines, but because it's the foundation upon which successful AI strategies are built.

The winners in the AI era won't be determined by who has the best models. They'll be determined by who measures, tests, and iterates most effectively. Just as it was with web analytics a quarter-century ago, so it will be with AI measurement in the year ahead.

The only question is: will your organization be ready?

About Winning Variant

Winning Variant, a Snowflake Startup Program partner and 2025 Snowflake Startup Challenge Finalist, is a software company based out of Phoenix, AZ that focuses on providing AI impact visibility tooling. Many use Winning Variant's software to gain visibility into the business impacts of their AI/ML initiatives.